{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fbc7ee6f-a7c9-4c2e-a206-ade0b9ace7e6",
   "metadata": {},
   "source": [
    "#  神经网络"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04680fee-dec1-41f1-81e1-3385c97aee4c",
   "metadata": {},
   "source": [
    "## 考试内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c63a3d0a-0895-4446-b2e9-83d12fd9d5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "15+15+40+30\n",
    "选择+填空+简答+编程\n",
    "1. 有10分左右超纲\n",
    "2. 不考公式推导\n",
    "3. 机器学习-基础概念\n",
    "    SKLearn- 梯度下降实现收敛\n",
    "    回归：linear Reg...SVM(支持向量，支持向量机)\n",
    "    分类:Logical Regresssion,SVM，KNN（K近邻），DT（决策树），RF（回归森林），MLP（NN）（神经网络）\n",
    "    降维：PCA（主成分分析）\n",
    "    聚类：Kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac62312d-28a6-4b2f-a8d6-4cc6ceb78747",
   "metadata": {},
   "outputs": [],
   "source": [
    "1.知道方法-教材位置\n",
    "    组织整理笔记：某方法对应的页数/概念对应的页数（无原话）\n",
    "2.机器学习内容\n",
    "    1.打开文件+读出数据（get dummy,standaedscaler,归一，独热）\n",
    "     构建数据集（spoit）\n",
    "    2.选择所需要的模型，创建模型的实例\n",
    "     拟合（fit）\n",
    "    3.predict\n",
    "    4.评价\n",
    "3.编程题：把作业看一遍\n",
    "    组织整理笔记：某方法出现在某处\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd1c1fd5-a3c9-4d23-bfe0-aa0ee0860ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "1.准备考试电源（线，插线板）\n",
    "2.不允许使用网络"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1b87355-2316-420e-8229-f4868a391384",
   "metadata": {},
   "source": [
    "## 课堂训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9231a55-a759-4b58-b202-3e9a7b531111",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>lris</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
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       "      <td>4.6</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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      "text/plain": [
       "     1    2    3    4         lris\n",
       "0  5.1  3.5  1.4  0.2  Iris-setosa\n",
       "1  4.9  3.0  1.4  0.2  Iris-setosa\n",
       "2  4.7  3.2  1.3  0.2  Iris-setosa\n",
       "3  4.6  3.1  1.5  0.2  Iris-setosa\n",
       "4  5.0  3.6  1.4  0.2  Iris-setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 利用pandas导入csv数据，查看前5行导入结果看是否正常\n",
    "import pandas as pd\n",
    "credit_df = pd.read_csv(\"d:/work/homework/irisdata.txt\", header=None)\n",
    "#源DataFrame中是没有索引的，此处加上索引\n",
    "new_column_names =['1','2','3','4','lris']\n",
    "credit_df.columns = new_column_names\n",
    "credit_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed69cd2f-fddb-49aa-934c-63d1165f407c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = credit_df.drop('lris',axis=1)\n",
    "y = credit_df['lris']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "25d578c6-8238-4b98-90c2-5f928c25a295",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 下面将数据分成训练集和测试集\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ece7e73-4f6e-4054-8d19-a7edc2698bb7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5.8</td>\n",
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       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
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       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>5.8</td>\n",
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       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
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       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
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      "text/plain": [
       "       1    2    3    4\n",
       "121  5.6  2.8  4.9  2.0\n",
       "26   5.0  3.4  1.6  0.4\n",
       "114  5.8  2.8  5.1  2.4\n",
       "23   5.1  3.3  1.7  0.5\n",
       "67   5.8  2.7  4.1  1.0\n",
       "..   ...  ...  ...  ...\n",
       "20   5.4  3.4  1.7  0.2\n",
       "70   5.9  3.2  4.8  1.8\n",
       "3    4.6  3.1  1.5  0.2\n",
       "118  7.7  2.6  6.9  2.3\n",
       "9    4.9  3.1  1.5  0.1\n",
       "\n",
       "[112 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "99f9aabc-953a-49ae-a657-e513b04c1cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69f74896-fb4d-4634-b8ed-f6bee48f2c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd833e12-d3c2-4499-90eb-5f571a3f3246",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled = scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e67fbe1a-fc05-4e2f-b17b-8b798328abff",
   "metadata": {},
   "outputs": [
    {
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       "       [-1.31482288,  0.31585613, -1.31077903, -1.21620341],\n",
       "       [-0.34120197, -1.54603265,  0.0715868 , -0.08631121],\n",
       "       [ 0.75412155,  0.31585613,  0.9010063 ,  1.42021172],\n",
       "       [ 0.2673111 , -0.38235216,  0.45864924,  0.4158631 ],\n",
       "       [ 1.60603985, -0.14961606,  1.2327741 ,  1.16912456],\n",
       "       [-1.19312027, -0.14961606, -1.2554844 , -1.34174698],\n",
       "       [ 0.51071633, -0.61508826,  0.62453314,  0.79249383],\n",
       "       [ 0.63241894, -0.84782435,  0.67982777,  0.79249383],\n",
       "       [ 0.87582417, -0.14961606,  1.17747947,  1.29466814],\n",
       "       [-0.82801242,  1.71227271, -0.97901123, -0.96511625],\n",
       "       [-1.31482288,  0.31585613, -1.14489513, -1.21620341],\n",
       "       [-0.46290458,  1.47953662, -1.20018977, -1.21620341],\n",
       "       [ 1.11922939,  0.08312003,  0.5692385 ,  0.4158631 ],\n",
       "       [-1.19312027,  0.78132833, -1.14489513, -1.21620341],\n",
       "       [-0.94971504, -0.14961606, -1.14489513, -1.21620341],\n",
       "       [ 1.72774246,  0.31585613,  1.28806874,  0.79249383],\n",
       "       [-0.34120197,  1.01406442, -1.31077903, -1.21620341],\n",
       "       [-1.67993072, -0.38235216, -1.2554844 , -1.21620341],\n",
       "       [ 0.2673111 , -0.14961606,  0.62453314,  0.79249383],\n",
       "       [ 0.75412155,  0.08312003,  1.01159557,  0.79249383],\n",
       "       [ 2.57966076,  1.71227271,  1.50924727,  1.04358099],\n",
       "       [-0.94971504,  1.01406442, -1.14489513, -0.7140291 ],\n",
       "       [ 1.11922939, -1.31329655,  1.17747947,  0.79249383],\n",
       "       [ 0.63241894, -0.61508826,  0.79041704,  0.4158631 ],\n",
       "       [ 0.75412155, -0.38235216,  0.34805997,  0.16477595],\n",
       "       [-0.46290458,  0.78132833, -1.0896005 , -1.21620341],\n",
       "       [ 0.14560848,  0.31585613,  0.62453314,  0.79249383],\n",
       "       [-1.43652549,  0.08312003, -1.20018977, -1.21620341],\n",
       "       [ 2.33625553, -1.08056045,  1.78572044,  1.42021172],\n",
       "       [-1.07141765,  0.08312003, -1.20018977, -1.34174698]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now apply the transformations to the data:\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0d05cc4-b112-452b-a3d7-e53634a3ab55",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7fe2c391-711c-4832-ab9c-623208f5b1cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MLPClassifier(hidden_layer_sizes=(4,3),max_iter=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40ba93b4-ed08-4512-bf5c-1b03bdfe9950",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:684: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(hidden_layer_sizes=(4, 3), max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(hidden_layer_sizes=(4, 3), max_iter=500)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(hidden_layer_sizes=(4, 3), max_iter=500)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9207fda1-ee27-478a-a7c4-de9fc1cec88f",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = mlp.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7337a84a-62f7-4ed4-b027-d6f53494bd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da2e2ac0-d4dc-49ef-8116-bbe3f7a82cc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8  0  0]\n",
      " [ 0  9 12]\n",
      " [ 0  0  9]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix(y_test,predictions), display_labels=['0','1','2'])\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a877f712-3dd9-416e-9697-2d6e0a286473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         8\n",
      "Iris-versicolor       1.00      0.43      0.60        21\n",
      " Iris-virginica       0.43      1.00      0.60         9\n",
      "\n",
      "       accuracy                           0.68        38\n",
      "      macro avg       0.81      0.81      0.73        38\n",
      "   weighted avg       0.86      0.68      0.68        38\n",
      "\n",
      "[[ 8  0  0]\n",
      " [ 0  9 12]\n",
      " [ 0  0  9]]\n"
     ]
    }
   ],
   "source": [
    "# 利用模型对测试集进行预测，输出target预测标签值和概率\n",
    "y_test_pred = mlp.predict(X_test)\n",
    "y_test_prob = mlp.predict_proba(X_test)\n",
    "\n",
    "# 分类评估汇总报告classification_report\n",
    "print(classification_report(y_test,y_test_pred))\n",
    "\n",
    "# 误分类矩阵 confusion_matrix\n",
    "print(confusion_matrix(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1050b8a6-e3b2-4ddc-9382-f68ca955f5ea",
   "metadata": {},
   "source": [
    "## 课堂练习2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "26f3639e-af6c-4dc2-90aa-be976f58fffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入数据\n",
    "import pandas as pd\n",
    "wine = pd.read_csv('wine.txt', names = [\"Cultivator\", \"Alchol\", \"Malic_Acid\", \"Ash\", \"Alcalinity_of_Ash\", \"Magnesium\", \"Total_phenols\", \"Falvanoids\", \"Nonflavanoid_phenols\", \"Proanthocyanins\", \"Color_intensity\", \"Hue\", \"OD280\", \"Proline\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "71fd1792-be5f-44dd-89bb-b5b8d3abc1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cultivator</th>\n",
       "      <th>Alchol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity_of_Ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total_phenols</th>\n",
       "      <th>Falvanoids</th>\n",
       "      <th>Nonflavanoid_phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color_intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cultivator  Alchol  Malic_Acid   Ash  Alcalinity_of_Ash  Magnesium  \\\n",
       "0           1   14.23        1.71  2.43               15.6        127   \n",
       "1           1   13.20        1.78  2.14               11.2        100   \n",
       "2           1   13.16        2.36  2.67               18.6        101   \n",
       "3           1   14.37        1.95  2.50               16.8        113   \n",
       "4           1   13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total_phenols  Falvanoids  Nonflavanoid_phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color_intensity   Hue  OD280  Proline  \n",
       "0             5.64  1.04   3.92     1065  \n",
       "1             4.38  1.05   3.40     1050  \n",
       "2             5.68  1.03   3.17     1185  \n",
       "3             7.80  0.86   3.45     1480  \n",
       "4             4.32  1.04   2.93      735  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据\n",
    "wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "386c340b-3ef0-43b6-a346-64bb6205696f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = wine.drop('Cultivator',axis=1)\n",
    "y = wine['Cultivator']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae85df84-00e1-4e2c-8483-9b28c957132c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#将 DataFrame df 转换为一个 NumPy 数组，并将这个数组赋值给变量 X_ori。.values 属性用于从 DataFrame 中提取底层的 NumPy 数组\n",
    "X_ori=df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "98a085c0-095e-4962-beef-37343281a03f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "std = StandardScaler()\n",
    "X = std.fit_transform(X_ori*1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9fa093ff-7db5-4c75-9572-a01742525ba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "from scipy.cluster import hierarchy\n",
    "dendro=hierarchy.dendrogram(hierarchy.linkage(X,method='ward'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4cf1a707-5558-4d18-bf2e-db9b72a9488a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'wcss: sum of dist. of sample to their closest cluster center')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "for i in range(1,10):\n",
    "    kmeans=KMeans(n_clusters=i,init='k-means++',)\n",
    "    kmeans.fit(X)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "plt.plot(range(1,10),wcss)\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('No. of cluster')\n",
    "plt.ylabel('wcss: sum of dist. of sample to their closest cluster center' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "209b79bc-a943-4e7d-a019-8d346fafc6d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "kmeans_1=KMeans(n_clusters=3)\n",
    "kmeans_1.fit(X)\n",
    "cluster_pred=kmeans_1.predict(X)\n",
    "cluster_pred_2=kmeans_1.labels_\n",
    "cluster_center=kmeans_1.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "edcff902-1885-4491-b3de-ca3130144785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 聚类结果\n",
    "cluster_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b978a840-4dbb-4b2b-9f1d-fdbbea5c825a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ea220a1-673f-4b81-acf8-c26740a6f99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.92607185, -0.39404154, -0.49451676,  0.17060184, -0.49171185,\n",
       "        -0.07598265,  0.02081257, -0.03353357,  0.0582655 , -0.90191402,\n",
       "         0.46180361,  0.27076419, -0.75384618],\n",
       "       [ 0.16490746,  0.87154706,  0.18689833,  0.52436746, -0.07547277,\n",
       "        -0.97933029, -1.21524764,  0.72606354, -0.77970639,  0.94153874,\n",
       "        -1.16478865, -1.29241163, -0.40708796],\n",
       "       [ 0.83523208, -0.30380968,  0.36470604, -0.61019129,  0.5775868 ,\n",
       "         0.88523736,  0.97781956, -0.56208965,  0.58028658,  0.17106348,\n",
       "         0.47398365,  0.77924711,  1.12518529]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 类中心点\n",
    "cluster_center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1e8a3280-919e-4d2a-802d-0d48bd5e625a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster = pd.DataFrame(cluster_pred,columns=['cluster'])\n",
    "df_cluster = pd.concat([df,cluster],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e0fe4b87-4149-45c2-beb1-bba2c888cdec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alchol</th>\n",
       "      <th>Malic_Acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity_of_Ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total_phenols</th>\n",
       "      <th>Falvanoids</th>\n",
       "      <th>Nonflavanoid_phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color_intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280</th>\n",
       "      <th>Proline</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Alchol  Malic_Acid   Ash  Alcalinity_of_Ash  Magnesium  Total_phenols  \\\n",
       "0   14.23        1.71  2.43               15.6        127           2.80   \n",
       "1   13.20        1.78  2.14               11.2        100           2.65   \n",
       "2   13.16        2.36  2.67               18.6        101           2.80   \n",
       "3   14.37        1.95  2.50               16.8        113           3.85   \n",
       "4   13.24        2.59  2.87               21.0        118           2.80   \n",
       "\n",
       "   Falvanoids  Nonflavanoid_phenols  Proanthocyanins  Color_intensity   Hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   OD280  Proline  cluster  \n",
       "0   3.92     1065        2  \n",
       "1   3.40     1050        2  \n",
       "2   3.17     1185        2  \n",
       "3   3.45     1480        2  \n",
       "4   2.93      735        2  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cluster.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "60141e79-ac0d-4d46-9a55-2b8400934f62",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = df_cluster.groupby('cluster').mean()\n",
    "cluster_center_ori = mean\n",
    "cluster_center_ori = cluster_center_ori.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "891a7c06-5788-4e7f-9dbd-65275a060bbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.22509231e+01, 1.89738462e+00, 2.23123077e+00, 2.00630769e+01,\n",
       "        9.27384615e+01, 2.24769231e+00, 2.05000000e+00, 3.57692308e-01,\n",
       "        1.62415385e+00, 2.97307692e+00, 1.06270769e+00, 2.80338462e+00,\n",
       "        5.10169231e+02],\n",
       "       [1.31341176e+01, 3.30725490e+00, 2.41764706e+00, 2.12411765e+01,\n",
       "        9.86666667e+01, 1.68392157e+00, 8.18823529e-01, 4.51960784e-01,\n",
       "        1.14588235e+00, 7.23470586e+00, 6.91960784e-01, 1.69666667e+00,\n",
       "        6.19058824e+02],\n",
       "       [1.36767742e+01, 1.99790323e+00, 2.46629032e+00, 1.74629032e+01,\n",
       "        1.07967742e+02, 2.84758065e+00, 3.00322581e+00, 2.92096774e-01,\n",
       "        1.92209677e+00, 5.45354839e+00, 1.06548387e+00, 3.16338710e+00,\n",
       "        1.10022581e+03]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_center_ori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bb9ef44d-e999-45ae-b8d8-6162aefe7f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the clusters\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(X[cluster_pred==0,0],X[cluster_pred==0,1], s = 100, c = 'red', label ='cluster 1' )\n",
    "plt.scatter(X[cluster_pred==1,0],X[cluster_pred==1,1], s = 100, c = 'blue', label ='cluster 2' )\n",
    "plt.scatter(X[cluster_pred==2,0],X[cluster_pred==2,1], s = 100, c = 'green', label ='cluster 3' )\n",
    "plt.scatter(cluster_center[:,0],cluster_center[:,1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of customers')\n",
    "plt.xlabel('Monthly Income -standard')\n",
    "plt.ylabel('Spending Score (1-100)-standard')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "80203068-c071-4271-a298-993d23b43547",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the clusters\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(X_ori[cluster_pred==0,0],X_ori[cluster_pred==0,1], s = 100, c = 'red', label ='cluster 1' )\n",
    "plt.scatter(X_ori[cluster_pred==1,0],X_ori[cluster_pred==1,1], s = 100, c = 'blue', label ='cluster 2' )\n",
    "plt.scatter(X_ori[cluster_pred==2,0],X_ori[cluster_pred==2,1], s = 100, c = 'green', label ='cluster 3' )\n",
    "plt.scatter(cluster_center_ori[:,0],cluster_center_ori[:,1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "plt.title('Clusters of customers')\n",
    "plt.xlabel('Monthly Income')\n",
    "plt.ylabel('Spending Score (1-100)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a537e860-b5eb-4c49-b5bc-526144a3175b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "D:\\anacoda\\anacoda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from kimi\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# 假设X是已经标准化处理过的特征数据\n",
    "kmeans = KMeans(n_clusters=3, random_state=42)\n",
    "kmeans.fit(X)\n",
    "\n",
    "# 获取聚类中心\n",
    "centers = kmeans.cluster_centers_\n",
    "\n",
    "# 绘制聚类结果\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_, cmap='viridis', marker='o', edgecolor='k', s=50)\n",
    "\n",
    "# 标识聚类中心\n",
    "plt.scatter(centers[:, 0], centers[:, 1], c='red', marker='X', s=200, label='Centroids')\n",
    "\n",
    "plt.title('K-means Clustering of Wine Dataset')\n",
    "plt.xlabel('Feature 1 (scaled)')\n",
    "plt.ylabel('Feature 2 (scaled)')\n",
    "plt.colorbar(label='Cluster Label')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae0c0ece-3cb9-4c7e-9405-1b5e49fa910e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
